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Partial differential equation transform—Variational formulation and Fourier analysis
Author(s) -
Wang Yang,
Wei GuoWei,
Yang Siyang
Publication year - 2011
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.1452
Subject(s) - partial differential equation , mathematics , algorithm , fourier transform , energy functional , discrete fourier transform (general) , image processing , mathematical optimization , mathematical analysis , fractional fourier transform , computer science , fourier analysis , image (mathematics) , artificial intelligence
SUMMARY Nonlinear partial differential equation (PDE) models are established approaches for image/signal processing, data analysis, and surface construction. Most previous geometric PDEs are utilized as low‐pass filters, which give rise to image trend information. In an earlier work, we introduced mode decomposition evolution equations (MoDEEs), which behave like high‐pass filters and are able to systematically provide intrinsic mode functions (IMFs) of signals and images. Because of their tunable time–frequency localization and perfect reconstruction, the operation of MoDEEs is called a PDE transform. By appropriate selection of PDE transform parameters, we can tune IMFs into trends, edges, textures, noise, and so forth, which can be further utilized in the secondary processing for various purposes. This work introduces variational formulation, performs the Fourier analysis, and conducts biomedical and biological applications of the proposed PDE transform. The variational formulation offers an algorithm to incorporate two image functions and two sets of low‐pass PDE operators in the total energy functional. Two low‐pass PDE operators have different signs, leading to energy disparity, while a coupling term, acting as a relative fidelity of two image functions, is introduced to reduce the disparity of two energy components. We construct variational PDE transforms by using Euler–Lagrange equation and artificial time propagation. Fourier analysis of a simplified PDE transform is presented to shed light on the filter properties of high‐order PDE transforms. Such an analysis also offers insight on the parameter selection of the PDE transform. The proposed PDE transform algorithm is validated by numerous benchmark tests. In one selected challenging example, we illustrate the ability of PDE transform to separate two adjacent frequencies of sin ( x ) and sin(1.1 x ). Such an ability is due to PDE transform's controllable frequency localization obtained by adjusting the order of PDEs. The frequency selection is achieved either by diffusion coefficients or by propagation time. Finally, we explore a large number of practical applications to further demonstrate the utility of the proposed PDE transform. Copyright © 2011 John Wiley & Sons, Ltd.